DESCRIPTION: Development of an Intelligent Sample Introduction System for Mass Spectrometry There is a pressing need for new technologies that enable rapid measurements without significant compromise to the analytical data. Herein, we propose to develop a robust mass spectrometry (MS) based surface sampling device that enables direct analysis of a variety of materials including biological tissue and live microorganisms that promises to deliver on this objective. The basis for this device is the Liquid Micro junction Surface Sampling Probe (LMJSSP), a set of coaxial microfluidic capillaries that are pumped and aspirated to bring solvent to and away from a surface of interest, where micro extraction takes place at the interface between the probe and the sample. Prosolia has licensed the LMJSSP technology from AB Sciex and will partner with the original inventor at Oak Ridge National Laboratory via the NIH Lab to Marketplace program to create an advanced prototype towards a commercially viable product by making the technology more robust and user-friendly for applications in various bio analytical workflows. The tasks of this project can be divided into three Aims. The first and second Aims are to ensure that the hardware of the device is sufficiently easy to use for commercial viability. This includes characterization of the present prototype and exploration of a few promising methods for automatic control of matched liquid flows required for a continuous extractive junction to persist between probe and surface. Additionally, establishing a mechanism for maintaining an ideal distance between the probe and surface will be critical for successful implementation. Upon establishing methods for flow and height management with optimized control and reasonable potential for manufacture, these hardware modifications will be used to determine the analytical conditions for instrument operation. Specifically, standards will be created to determine the profile and efficiency of extraction under a variety of conditions (including within different laboratories). These will determine the expected limits for potential use, maintenance, and support to ensure robust operations calibrated for a variety of methodologies. During this section of work, investigations into potential increases in resolution from the standard probing arrangement via smaller variants will be performed to identify analytical metrics at histologically relevant sampling sizes of interest to diagnostic and biomarker researchers. Early experiments using another MS-based surface sampling technique, Desorption Electrospray Ionization, for direct bio analysis of live microorganisms were able to generate spectral profiles for bacterial colonies; however, the process to generate these results required frequent cleaning and exchanging of instrument capillaries due to fouling with bacteria that were blown off the surface and into the instrument inlet. Preliminary spectra obtained using our prototype LMJSSP system have been collected in a much more facile manner, and have shown some particularly interesting features for distinguishing and characterizing different species and strains of bacteria. However, more fingerprints must be acquired in order to determine statistical significance and applicability of these data. In addition, we will establish he effects of sampling at various growth points and stress conditions in order to generate a potential library for automated classification. After implementing changes for automation and optimization, our prototype for direct analysis of surfaces, will be a simple sample introduction system transitioning from an academic prototype used by a few to a commercial prototype that can be deployed to many. By hewing to the milestones we propose: making appropriate hardware changes, determining analytical metrics corresponding to solvents and probe sizes, and gathering a statistically relevant number of profiles for bacterial analysis, the capacity for hands-off microorganism profiling will have been assessed. As such our phase II proposal will include further hardware refinements, a broader selection of samples, enhanced data integration, and processing software.